Here is a snapshot 📸 of your model outputs for run ID 20250310_220042EDT, from config config_sample_2pop_inference.yml, stored in model_output.
SEIR model outputThese are the outputs for the compartmental epidemic model, stored in
the seir directory, which track the prevalence and
incidence of individuals in each model compartment over time.
Incidence values are per day.
## [1] "Assuming inference run with files in/global/final"
## [1] "Importing seir files (n = 5):"
## [1] "Assuming inference run with files in/global/final"
## [1] "Importing llik files (n = 5):"
Total number of individuals in each infection state over time
(compartments defined by infection_stage), aggregated
across other strata. Plotted for slot 5 which has the highest total
likelihood over all subpopulations (if inferrence was run) or was
randomly chosen (if no inference).
HOSP model outputThese are the outputs for the observational (“outcomes”) model,
stored in the hosp directory, which tracks the incidence
and prevalence of individuals with defined observed disease outcomes
over time.
Total number of individuals with each outcome over time, aggregated across other strata (only outcomes without an “_” specifying a stratification are plotted). If more than one simulation (slot) was run, results are plotted for slot 5 which has the highest total likelihood over all subpopulations (if inference was run) or was randomly chosen (if no inference). Incidence values are per day.
If inference was run, only some of these outcomes may have been used in inference, and the outcomes may have been aggregated to a longer time period (e.g., weeks, months). Inference-specific outcomes, along with the data they were compared to, are shown in later plots.
[1] “Assuming inference run with files in/global/final” [1] “Importing hosp files (n = 5):”
The inference method specified that the model be fit to sum_hosp, with aggregation over period: 1 weeks. Plotted for slot 5 which has the highest total likelihood over all subpopulations (if inference was run) or was randomly chosen (if no inference).
[1] “Assuming inference run with files in/global/final” [1] “Importing hosp files (n = 5):”
The inference method specified that the model be fit to sum_hosp, with aggregation over period: 1 weeks. In total 5 slots ran successfully.
## Inference-specific outcomes - by likelihood{.tabset}
The inference method specified that the model be fit to sum_hosp, with aggregation over period: 1 weeks. In total 5 slots ran successfully.
This section plots the top 5 and bottom 5 log likelihoods for each subpopulation.
SNPI model outputThese are the parameters that define time-dependent modifications to
the infection model parameters, and are stored in the snpi
directory.
If inference is run, parameters are the final values at the end of
all MCMC iterations, colored by their likelihoods in a given
subpopulation.
The accepted value of the parameter for each iteration of the MCMC algorithm, colored by their likelihood in a given subpopulation. If more than 5 slots were run, we will plot only the top 5 and bottom 5 log likelihoods for each subpopulation.
The accepted value of the parameter for each iteration of the MCMC algorithm, for both the chimeric and global chain, in a given subpopulation. Plotted for slot 5 which has the highest total likelihood over all subpopulations (if inference was run) or was randomly chosen (if no inference).
HNPI model outputThis shows the parameters associated with your outcomes model, for all subpopulations.
If inference is run, parameters are the final values at the end of all MCMC iterations, coloured by their likelihoods in a given subpopulation.
The accepted value of the parameter for each iteration of the MCMC algorithm, colored by their likelihood in a given subpopulation. If more than 5 slots were run, we will plot only the top 5 and bottom 5 log likelihoods for each subpopulation.
The accepted value of the parameter for each iteration of the MCMC algorithm, for both the chimeric and global chain, in a given subpopulation. Plotted for slot 5 which has the highest total likelihood over all subpopulations (if inference was run) or was randomly chosen (if no inference).
LLIK model outputThis plot shows the binary acceptance decision for each MCMC
iteration (accept), the probability of acceptance for that
acceptance decision (accept_prob), the running average
acceptance probability (accept_avg), and the likelihood.
Chimeric values are subpopulation specific - there are
likely more acceptances as well as acceptances that can increase
subpop-specific likelihood while not changing the total likelihood.
Global acceptances occur for all subpopulations together,
and will always result in the total likelihood increasing, but could
result in decreases in the subpop-specific likelihood.
## [1] "Assuming inference run with files in/global/intermediate"
## [1] "Importing llik files (n = 760):"
## [1] "Assuming inference run with files in/chimeric/intermediate"
## [1] "Importing llik files (n = 760):"